• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Fu Wen-Tao (Fu Wen-Tao.) | Qiao Jun-Fei (Qiao Jun-Fei.) (学者:乔俊飞) | Han Gai-Tang (Han Gai-Tang.) | Meng Xi (Meng Xi.)

收录:

CPCI-S

摘要:

In this paper, a novel kind of the dissolved oxygen (DO) concentration control system was proposed based on the T-S fuzzy neural network. The proposed T-S fuzzy neural network controller was used to control the DO concentration in the Benchmark Simulation Model No. 1 (BSM1) wastewater treatment platform. The parameters of the neural network were adjusted online through the error back propagation algorithm to get the minimum error. By adjusting the learning rate online, the convergence speed of the system was accelerated, and then the DO concentration in the wastewater treatment system was controlled fast and efficiently in real-time. Compared with BP and PID controllers through the digital simulation, the results showed that the control effect of the DO concentration based on T-S fuzzy neural network control system was better. Besides, the test results under three kinds of weather condition showed that better adaptability and robustness were also gained in this control system.

关键词:

BSM1 model Dissolved Oxygen Concentration control T-S Fuzzy Neural Network Wastewater treatment

作者机构:

  • [ 1 ] [Fu Wen-Tao]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China
  • [ 2 ] [Qiao Jun-Fei]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China
  • [ 3 ] [Han Gai-Tang]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China
  • [ 4 ] [Meng Xi]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China

通讯作者信息:

  • [Fu Wen-Tao]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China

查看成果更多字段

相关关键词:

来源 :

2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

ISSN: 2161-4393

年份: 2015

语种: 英文

被引次数:

WoS核心集被引频次: 3

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

在线人数/总访问数:1887/2982829
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司